Mapping of the Spatial Scope and Water Quality of Surface Water Based on the Google Earth Engine Cloud Platform and Landsat Time Series

Abstract

Surface water is an important parameter for water resource management and terrestrial water circulation research that is closely related to human production and livelihood. With the rapid development of remote sensing technology and cloud computing platforms, the use of remote sensing technology for large-scale and long-term surface water monitoring and investigation has become a research trend. Based on the Google Earth Engine (GEE) cloud platform and Landsat series satellite data, in this study, the Emergency Geomatics Service (EGS) operational surface water mapping algorithm and water index masking were utilized to extract the spatial scope of the water body. The validated models of the Secchi disk depth (SDD), chlorophyll-a (Chl-a) and suspended solids (SS) concentration were applied to water quality parameter inversion and water quality evaluation. Surface water extent extraction and water quality maps were created to analyze the spatial distribution of the water body and the spatial–temporal evolution characteristics of the water quality parameters. A verification experiment was carried out with the surface water in Zhejiang Province as the research object. The results show that the surface water in the study area from 1990 to 2022 could be accurately extracted. The kappa coefficients were all greater than 0.90, and the overall accuracies of the extractions were greater than 95.31%. From 1990 to 2022, the total surface water area in Zhejiang Province initially decreased and then increased. The minimum water area of 2027.49 km2 occurred in 2005, and the maximum water area of 2614.96 km2 occurred in 2020, with an annual average variation of 193.92 km2. Since 2015, the proportion of high SS and Chl-a concentrations, and low SDD water bodies in Zhejiang Province have decreased, and the proportion with better water quality has increased significantly. The spatial distribution map of the surface water and the inversion results of the water quality parameters obtained in this study provide a valuable reference and guidance for regional water resource management, disaster monitoring and early warning, environmental protection, and aquaculture.

Inversion of Chlorophyll-a Concentrations in Chaohu Lake Based on GF-1 WFV Images

“…Diao et al [15] used GF-1 WFV images as the data source, adopted an empirical model inversion algorithm, correlated the reflectances of a single band or combination of bands with the measured chlorophyll-a concentrations of the water body, selected the three-band model with the largest coefficient of determination as the inversion model to validate the accuracy of the model, and applied the model to the remote sensing inversion of the chlorophyll-a concentrations in Nansihu Lake. Jin et al [16] selected a single-band model consisting of the red bands as the inversion model for their study based on previous research results and inverted the chlorophyll-a concentrations in Zhejiang Province from 1990 to 2022 based on Landsat series data. Using Sentinel-2 images, Zhao et al [17] developed an OWT-based chlorophyll-a concentration inversion model and achieved dynamic monitoring and analysis of the chlorophyll-a concentrations in the world's 3067 largest lakes (≥50 km 2 ) through the use of the Google Earth Engine platform.…”

Monitoring of Low Chl-a Concentration in Hulun Lake Based on Fusion of Remote Sensing Satellite and Ground Observation Data

“…Remote sensing monitoring offers advantages over typical artificial sampling for monitoring Chl-a concentration due to its low cost, high efficiency, and continuous space-time scale. Researchers have successfully inverted Chl-a in several regions using different remote sensing satellites and sensors, including Landsat [4][5][6], Modis [7,8], Sentinel-2 [9][10][11], and others. The Sentinel-2 satellite is crucial in water quality inversion studies because of its high resolution and frequent revisit cycle compared to other satellite platforms.…”